Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance
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LDR | 00000cab a2200000 4500 | ||
001 | MAP20240013455 | ||
003 | MAP | ||
005 | 20240830110145.0 | ||
008 | 240830e20240815che|||p |0|||b|eng d | ||
040 | $aMAP$bspa$dMAP | ||
084 | $a219 | ||
100 | $0MAPA20170013198$aBücher, Axel | ||
245 | 1 | 0 | $aCombined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance$cAxel Bücher & Alexander Rosenstock |
520 | $aUsually, the actuarial problems of predicting the number of claims incurred but not reported (IBNR) and of modelling claim frequencies are treated successively by insurance companies. New micro-level methods designed for large datasets are proposed that address the two problems simultaneously. The methods are based on an elaborated occurrence process model that includes both a claim intensity model and a claim development model. The influence of claim feature variables is modelled by suitable neural networks. Extensive simulation experiments and a case study on a large real data set from a motor legal insurance portfolio show accurate predictions at both the aggregate and individual policy level, as well as appropriate fitted models for claim frequencies. Moreover, a novel alternative approach combining data from classic triangle-based methods with a micro-level intensity model is introduced and compared to the full micro-level approach | ||
650 | 4 | $0MAPA20080573867$aSeguro de salud | |
650 | 4 | $0MAPA20080586294$aMercado de seguros | |
650 | 4 | $0MAPA20080556495$aSiniestros | |
650 | 4 | $0MAPA20080573935$aSeguros no vida | |
650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
650 | 4 | $0MAPA20080557287$aAutomóviles | |
650 | 4 | $0MAPA20080603779$aSeguro de automóviles | |
700 | 1 | $0MAPA20240020996$aRosenstock, Alexander | |
773 | 0 | $wMAP20220007085$g15/08/2024 Volumen 14 - Número 2 - agosto 2024 , P. 623-655$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 | |
856 | $uhttps://link.springer.com/article/10.1007/s13385-024-00383-7 |